Designing a mood board is a creative tool designers often employ at the start of a new (interior) design project. The colors of a mood board represent important information about the envisioned interior designs. In this article, we focus on digital mood boards and we propose a novel method to determine the mood boards' key colors from a design perspective using dE‐means color clustering. This proposed algorithm includes a fixed initialization to overcome the non‐deterministic nature of traditional k‐means, and a merging step to ensure all colors in the calculated palette are at least a dE‐threshold apart. Quantitative and qualitative results demonstrate that the proposed algorithm outperforms existing methods in determining the key colors from a design perspective. Additionally, we introduce a novel method to visualize the calculated color palettes that takes into account the contribution of individual pixels to the construction of the calculated color palettes. Finally, we demonstrate how the proposed algorithm can be used to characterize color palettes for various design styles like modern, industrial and art deco.
The Color of the Year was first introduced by Pantone in 2000, and recently (the last decade) we saw the trend of introducing a Color of the Year being picked up by more and more companies. Paints and coatings companies typically select their colors of the year by extensive research by designers and trend experts, resulting in a plurality of colors being introduced as Color of the Year,
The digital representation of three dimensional objects with different materials has become common not only in the games and movie industry, but also in designer software, e-commerce and other applications. Although the rendered images often seem to be realistic, a closer look reveals that their color accuracy is often insufficient for critical applications. Storage of the angledependent color properties of metallic coatings and other gonioapparent materials demands large amounts of data. Apart from that, also rendering sparkle, gloss and other visual texture phenomena is still a subject of active research. Current approaches are computationally very demanding, and require manual ad-hoc setting of many model parameters. In this paper, we describe a new approach to solve these problems. We combine a multi-spectral physics-based approach to make BRDF representation more efficient. We also account for the common loss in color accuracy due to the varying technical specifications of displays, and we correct for the influence from ambient lighting. The rendering framework presented here is shown to be capable of rendering sparkle and gloss as well, based on objective measurement of these properties. This takes out the subjective phase of manual fine-tuning of model parameters that is characteristic for many current rendering approaches. A feasibility test with the new spectral rendering pipeline shows that is indeed able to produce realistic rendering of color, sparkle, gloss and other texture aspects. The computation time is small enough to make the rendering real-time on an iPad 2017, i.e. with low memory footprint and without high demands on graphic card or data storage.
Accurate measurements of reflectance and color require spectrophotometers with prices often exceeding $3000. Recently, new “color instruments” became available with much lower prices, thanks to the availability of inexpensive colorimetric sensors. We investigated the Node+ChromaPro and the Color Muse, launched in 2015 and 2016 by Variable Inc. Both instruments are colorimeters, combining a colorimetric sensor with LED lighting. We investigated color accuracy compared to a high-end spectrophotometer from BYK Gardner. With different sets of samples we find for the Node an average value of dECMC (1:1) = 1.50, and a maximum of 7.86, when comparing with the 45° geometry of the spectrophotometer. Utilizing measurement data on the Spectral Power Distributions of the LEDs, we developed three methods to improve color accuracy as compared to the spectrophotometer data. We used these methods on different sets of samples with various degrees of gloss, both for training the models underlying the methods and for independent tests of model accuracy. Average color accuracy of the Node+ChromaPro improves from dECMC (1:1) = 1.82 to 1.16 with respect to spectrophotometer data. The percentage of samples with dECMC (1:1) < 1.0 increases from 30.9% (uncorrected) to 64%. With the improved color accuracy, these sensors become useful for many more applications.
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